Do you have new EHR/EMR software for your practice? You need to find the right way to start migrating your EHR data, right? Migration of health data from the EHR to the broader EHR depends on clear guidelines and recognized research. It is a complex process for which there are no agreed standards. So, before starting the migration process, you should ask yourself a few questions to get started with the usability of your new EHR. Consider, for example, how to migrate existing data to the new EHR, who will lead this time-consuming project, and what information is needed. Also, what steps will be taken for any information lost during migration?

  1. What is patient data?
  2. What is patient data migration?
  3. Why should patient data be migrated?
  4. Patient data migration strategies
  5. What are the best practices for patient data migration?
  6. Challenges of patient data migration

What is patient data?

Patient data is medical information stored about individual patients. Patient data may include information about past and current health conditions or diseases, medical history, lifestyle choices, and genetic data. This also includes biometric data, which are measurable physical characteristics that can be verified by many devices. This type of patient data is also used in research to identify effective treatments, monitor drug safety, and generate new knowledge about diseases and their causes. Advances in information technology and the introduction of electronic medical records are expanding opportunities for researchers. Collecting and sharing patient medical data is especially important for improving our understanding of rare and genetic diseases and addressing the unmet needs of these patients. Most genetic disorders are rare. Due to this small number of patients, it is important to collect information from as many patients as possible. Patient data-driven research findings can be used to estimate disease burden, inform medical planning decisions, identify new treatments, and demonstrate their impact.

What is patient data migration?

Patient data migration is the process of transferring healthcare data from one system to another. This can include moving data from an electronic health record (EHR) or electronic medical record (EMR) system to a new EHR system or migrating data from one hospital or clinic to another. It is basically the process of moving information about the patients and their medical data from one storage place to another.

Data migration in the healthcare industry is driven by the need to move electronic medical records. Maintaining a system that stores and manages all patient-related information in a single, secure location requires the ability to move that data to more advanced storage with restricted access and analytics. Data migration is one of the phases of EHR implementation. EHR data migration is often driven by the need to relinquish legacy systems that have no effective capacity in the hospital business environment. Due to the unique challenges of the EHR data migration process, there are three healthcare data migration tools to choose from, depending on your use case and data end goal.

Why should patient data be migrated?

When small amounts of data from many patients are linked and aggregated, researchers and physicians will find new ways to predict or diagnose disease and improve the identification of clinical care. You can look for patterns in your data that help you develop strategies.

Patient data migration requires early planning for this main reason: The system was not designed and built to move such large amounts of data to another system at once, due to hardware and network limitations. During the transition, the data will be unavailable and inaccessible to healthcare providers and other stakeholders involved in the healthcare process. The risk of data loss increases when practices leave data in risky migration locations.

Patient Data Migration Strategies

There is more than one way to build a data migration strategy. An organization’s specific business needs and requirements will help establish priorities. However, most strategies fall into one of two categories: “big bang” or “trickle.”

“Big Bang” Migration

A “big bang” data migration completes the complete transfer within a limited time frame. A live system experiences downtime while data goes through its ETL process and migrates to a new database. Of course, the advantage of this method is that everything happens in a timed event that takes a relatively short time. However, the company has one of its resources offline, so the pressure can be enormous. This risks breaking implementation. If the “big bang” approach is best for your business, consider running the migration process before the actual event.

“Trickle” Migration

“Trickle” migrations complete the migration process in phases. During implementation, the old system and the new are run in parallel, which eliminates downtime or operational interruptions. Processes running in real-time can keep data continuously migrating.

Compared to the “big bang” approach, these implementations can be fairly complex in design. However, the added complexity usually reduces risks, rather than adding them.

What are the best practices for patient data migration?

  • Back up the data –If something goes wrong during implementation, you can’t afford to lose data. Make sure the backup resource exists and has been tested before proceeding.
  • Have a strategy –Too many data managers make plans and then discard them when the process gets out of hand. The migration process can be complicated and frustrating. So be prepared for this reality and follow your plan.
  • Conduct a lot of tests –Test your data migration throughout the planning and design stages, as well as during implementation and maintenance, to ensure the achievement of desired results.

If your organization is upgrading systems or moving to the cloud, data migration is on the horizon. It’s a big and important project, and the integrity of the data demands that it gets done correctly.

 Kickstart your data migration process by exploring Triyam’s software Fovea.  For more information, click here. 

Challenges of patient data migration

  1.  Lack of strategic planning – Be prepared. Lack of planning erodes confidence and prevents practices from reaping the rewards of the new system. The process of data migration in healthcare is complex.
  2. Data Quality – Data migration in the healthcare industry requires post-migration data quality assurance and an understanding of the data to be transformed and used. It is important to regularly update patient data and statistics so that we can provide reliable care to patients in need.
  3. Privacy and Data Compliance – By neglecting privacy and data compliance, a single data breach can cost healthcare providers millions of dollars. Data handlers can identify issues and quickly fix data sets before migrating data during healthcare transformations.